The overall objective of the present proposal is to develop, evaluate, distribute, and apply tools for the BrainMap Project, which provides the human brain mapping (HBM) community with data sets, computational tools, and informatics resources for quantitative meta-analyses and meta-analysis-based data interpretation. The development of coordinate-based, voxel-wise meta-analysis (CVM) has been a breakthrough for HBM, enabling statistically rigorous meta-analysis and systems-level modeling of published results. The HBM literature suitable for CVM meta-analysis was estimated in 2007 to be ~6,000 papers, with ~1000 new conforming papers being published each year. In this renewal, we propose numerous enhancements to widely used coordinate-based meta-analysis tools (Aim 1). Meta-analytic connectivity mapping (MACM) is a new application of CVM, which derives inter-regional connectivity maps from inter-study co-occurrence patterns. In this renewal, we propose to extend the functionality of our MACM tools, construct connectivity atlases through data mining, and validate these atlases by comparison to non-meta-analytic approaches (Aim 2). In BrainMap, behavioral meta-data are coded using a taxonomy developed and progressively refined by the BrainMap team. Recent application of independent component analysis (ICA) to BrainMap extracted intrinsic neural systems that were well, but not perfectly, discriminated by the BrainMap coding scheme. This observation suggests robust computational approaches both for providing a behavioral ontology for intrinsically connected networks and also for programmatic refinement of the BrainMap coding scheme (Aim 3). Finally, it has recently been demonstrated that CVM can be applied to voxel-based morphometry (VBM) structural neuroimaging studies. In Aim 4, we anticipate the rapid growth of VBM research by creating a BrainMap-like database of the standards- compliant VBM literature. These proposed enhancements and extensions of the BrainMap Project will allow this rich set of neuroinformatics tools to continue to meet the research and educational needs for knowledge discovery and data mining in the neuroimaging community. 1